Multimorbidity PhD Programme for Health Professionals
Multimorbidity is the presence of two or more long-term health conditions in a person, and is a major global public health problem of increasing prevalence.
The University of Edinburgh is part of a collaborative research programme which aims to meet challenges posed by increasing multimorbidity.
- higher mortality
- poorer quality of life
- reduced life expectancy
The Multimorbidity PhD Programme for Health Professionals, funded by Wellcome Trust, aims to create a generation of innovative world-leading researchers empowered with expertise in multimorbidity to prevent the condition and better treat those who are already suffering with it.
The programme is a collaboration between the Universities of Edinburgh, Glasgow, Dundee and St Andrews and is open to: doctors, nurses, pharmacists, dentists, clinical psychologists and allied health professionals.
Application Deadline of 10th January.
For further information about the programme, and to apply, please click here.
The projects to be supervised within the University of Edinburgh are as follows:
Quantifying drug harm in polypharmacy, multimorbidity and frailty: pharmaco-epidemiological and machine learning approaches in large routine datasets
Primary Supervisor: Prof Bruce Guthrie
Prescribed drugs have both high benefit and cause considerable harm in people with multimorbidity and polypharmacy, but our understanding of drug risks and harms is relatively narrow. For example, we have fairly good understanding of risks for some pairwise drug-drug interactions, but this is of limited relevance in polypharmacy where multiple drug-drug and drug-disease interactions are typically present. The student will choose an aspect of this complex problem to focus on, and develop analysis using and comparing pharmaco-epidemiological and machine learning methods. Examples of focus include examining the impact of shared adverse effects across multiple drugs, examining cumulative risk related to duration of treatment, and exploring how frailty and comorbidity mediate risk of harm. The student will have access to large, curated clinical datasets. The supervisors are two clinical academics interested in polypharmacy, prescribing safety and pharmaco-epidemiology, and a senior data scientist interested in developing and applying interpretable methods for analyzing the complexity of multimorbidity and polypharmacy. The supervisory team have excellent links with national guideline developers and medicine regulators. The student will therefore gain high-level expertise in applying complex quantitative methods in large datasets to address important clinical questions, and in translating findings into impact on understanding, policy and practice.
Multimorbidity, delirium, and outcomes in hospitalised patients: a big data project using routine clinical and national audit data
Primary Supervisor: Prof Alasdair MacLullich
People with multimorbidity are at much higher risk of emergency conditions requiring hospital admission. Hospitalised people with multimorbidity have worse outcomes including higher mortality and reduced level of function leading to increasing dependency at home or new care home admission. Delirium is a serious sudden-onset neuropsychiatric syndrome that affects around 1 in 5 people in hospital. People with delirium have poor outcomes including 3-fold 30-day mortality risk, and functional and cognitive decline. Multimorbidity is a risk factor for delirium, but the degree of the risk including the potential role of particular disease clusters (e.g. nervous system disorders) is understudied.
This project will examine two main questions: how multimorbidity influences delirium risk, and how delirium influences outcomes in people with multimorbidity. Two potential patient groups for study are medical emergencies and hip fracture patients. The student will have access to a uniquely strong combination of large scale data resources (>50000 patient records) to address these questions, including community and hospital data, national hip fracture audit data, and high quality clinical delirium ascertainment. The supervising team have expertise in analysing large datasets and in all clinical aspects of the project. The student will gain valuable experience of analysis of large healthcare datasets, and generating and sharing findings which have relevance for practice.
Multimorbidity in socially excluded populations: developing a complex intervention to reduce stigma and enhance practitioner empathy in primary care settings
Primary Supervisor: Prof Stewart Mercer
Groups that suffer from social exclusion, such as people experiencing homelessness (PEH), and people suffering from drug use disorders, commonly have high levels of complex needs including mental and physical multimorbidity. Such populations often ‘fail to engage’ with healthcare services, and perceived stigma plays an important role in this. Previous research has demonstrated the key importance of practitioner empathy in patient engagement and better health outcomes but this has not been investigated in socially excluded groups. The PhD will co-develop a primary care-based complex intervention to reduce stigma and enhance empathy towards patients suffering social exclusion with complex multimorbidity. The research will employ a mixed-methods approach, as well as systematic reviewing of the international literature. People with lived experience of social exclusion and primary care staff will be involved in the co-production of the intervention, which will be tested for feasibility. The supervisory team has extensive experience in primary care research, multimorbidity, deprivation, drug addiction, homelessness, and complex intervention development. They also have excellent links with policymakers, managers, and service providers and thus the student will gain from access to a range of expertise and advice regarding dissemination, translation of findings, and impact on policy and practice.
Interventions to improve mental health and wellbeing among older people with multiple conditions: a mixed methods approach
Primary Supervisors: Dr Lucy Stirland Prof Bruce Guthrie
Multimorbidity including physical and mental illnesses is common, especially among older people and those living in socioeconomic deprivation. Research in this area has called for enhanced mental health support both to improve wellbeing and to prevent the onset of mental illness in people with multiple chronic conditions. However, evidence is lacking on what are the most effective and acceptable interventions, and what services are currently offered to patients.
This PhD offers flexibility for the student to choose a specific area of focus, in terms of context (eg community services vs psychological interventions) and research questions and methods. All students will complete a systematic review and use existing routine data to understand the epidemiology of physical-mental health multimorbidity in older people in Scotland. Students will then choose which other aspects to focus on, including using more complex quantitative methods; service mapping and examination of inequalities in access; qualitative methods to understand older people and carers’ experience of wellbeing and what factors underpin or undermine it; and/or co-design methods co-produce potential interventions. Working closely with public partners will be an essential element of the research, and the student will gain experience in critical appraisal, data science and qualitative research methods matched to their future research interests.
Primary Supervisor: Dr Caroline Jackson
People diagnosed with severe mental illness (SMI) have a reduced life expectancy. This is largely due to natural causes of death of which cardiovascular disease is the most common. Robust cardiovascular risk scores are important for identifying high risk patients who would benefit most from treatment. Prediction of cardiovascular risk is usually based on age, sex, smoking status, hypertension and blood lipid profile, using cardiovascular risk scores. However, the available cardiovascular risk scores such as Framingham have been developed in populations without people with SMI and subsequently underestimate the risk in people with SMI which potentially leads to under treatment. Therefore tailored cardiovascular risk models for this vulnerable group or general population risk models with SMI status added to the predictors are necessary. Recently the QRISK3 model has been developed which is a general population score including SMI (not including depression) as a predictor. Before implementation in clinical practice external validation is required to assess performance in terms of discrimination and calibration in other settings. The aim of this PhD project is to evaluate the performance of QRISK3 in datasets from within and outside the UK and to expand SMI to include major depression.
Primary Supervisor: Dr Nazir Lone
Critical care patients have a high prevalence of multimorbidity which is associated with poorer outcomes. In addition, 20% of patients have a mental health comorbidity. The impact of co-existing mental and physical multimorbidity in the context of critical illness is unclear. In particular, its impact on care pathways, interventions and outcomes have not been previously investigated.
The overall aim of the studentship is to evaluate the impact of co-existing mental and physical multimorbidity on care pathways, acute care interventions, healthcare resource use and outcomes for critically ill patients in order to inform improvements in care quality.
During the studentship, a series of epidemiological analyses will be undertaken to determine associations between co-existing mental illness/physical multimorbidity and acute illness features as well as post-discharge recovery, underpinned by explicit causal frameworks. Subsequently, patterns in care pathways and how these relate to co-existing mental/physical multimorbidity will be explored using applied data science methods including unsupervised learning methods and spectral clustering.
The studentship will provide training in epidemiology, statistics, causal inference, and applied machine learning methods. The student will benefit from the vibrant, academic environment in the Usher Institute and synergistic learning from other multimorbidity work undertaken by the team (https://edin.ac/3CqoERz).